#define _USE_MATH_DEFINES // for M_PI

#include "common.h"

// third-party utilities
// use your favorite implementations
#define DR_WAV_IMPLEMENTATION

#include "dr_wav.h"

#define DR_MP3_IMPLEMENTATION

#include "dr_mp3.h"
#include <samplerate.h>
#include <cmath>
#include <cstring>
#include <fstream>
#include <regex>
#include <locale>
#include <codecvt>
#include <sstream>

#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif

// Function to check if the next argument exists
std::string get_next_arg(int &i, int argc, char **argv, const std::string &flag, gpt_params &params) {
  if (i + 1 < argc && argv[i + 1][0] != '-') {
    return argv[++i];
  } else {
    fprintf(stderr, "error: %s requires one argument.\n", flag.c_str());
    gpt_print_usage(argc, argv, params);
    exit(0);
  }
}

bool gpt_params_parse(int argc, char **argv, gpt_params &params) {
  for (int i = 1; i < argc; i++) {
    std::string arg = argv[i];

    if (arg == "-s" || arg == "--seed") {
      params.seed = std::stoi(get_next_arg(i, argc, argv, arg, params));
    } else if (arg == "-t" || arg == "--threads") {
      params.n_threads = std::stoi(get_next_arg(i, argc, argv, arg, params));
    } else if (arg == "-p" || arg == "--prompt") {
      params.prompt = get_next_arg(i, argc, argv, arg, params);
    } else if (arg == "-n" || arg == "--n_predict") {
      params.n_predict = std::stoi(get_next_arg(i, argc, argv, arg, params));
    } else if (arg == "-np" || arg == "--n_parallel") {
      params.n_parallel = std::stoi(get_next_arg(i, argc, argv, arg, params));
    } else if (arg == "--top_k") {
      params.top_k = std::stoi(get_next_arg(i, argc, argv, arg, params));
    } else if (arg == "--top_p") {
      params.top_p = std::stof(get_next_arg(i, argc, argv, arg, params));
    } else if (arg == "--temp") {
      params.temp = std::stof(get_next_arg(i, argc, argv, arg, params));
    } else if (arg == "--repeat-last-n") {
      params.repeat_last_n = std::stoi(get_next_arg(i, argc, argv, arg, params));
    } else if (arg == "--repeat-penalty") {
      params.repeat_penalty = std::stof(get_next_arg(i, argc, argv, arg, params));
    } else if (arg == "-b" || arg == "--batch_size") {
      params.n_batch = std::stoi(get_next_arg(i, argc, argv, arg, params));
    } else if (arg == "-c" || arg == "--context") {
      params.n_ctx = std::stoi(get_next_arg(i, argc, argv, arg, params));
    } else if (arg == "-ngl" || arg == "--gpu-layers" || arg == "--n-gpu-layers") {
      params.n_gpu_layers = std::stoi(get_next_arg(i, argc, argv, arg, params));
    } else if (arg == "--ignore-eos") {
      params.ignore_eos = true;
    } else if (arg == "-m" || arg == "--model") {
      params.model = get_next_arg(i, argc, argv, arg, params);
    } else if (arg == "-i" || arg == "--interactive") {
      params.interactive = true;
    } else if (arg == "-ip" || arg == "--interactive-port") {
      params.interactive = true;
      params.interactive_port = std::stoi(get_next_arg(i, argc, argv, arg, params));
    } else if (arg == "-h" || arg == "--help") {
      gpt_print_usage(argc, argv, params);
      exit(0);
    } else if (arg == "-f" || arg == "--file") {
      get_next_arg(i, argc, argv, arg, params);
      std::ifstream file(argv[i]);
      if (!file) {
        fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
        break;
      }
      std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt));
      if (params.prompt.back() == '\n') {
        params.prompt.pop_back();
      }
    } else if (arg == "-tt" || arg == "--token_test") {
      params.token_test = get_next_arg(i, argc, argv, arg, params);
    } else {
      fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
      gpt_print_usage(argc, argv, params);
      exit(0);
    }
  }

  return true;
}

void gpt_print_usage(int /*argc*/, char **argv, const gpt_params &params) {
  fprintf(stderr, "usage: %s [options]\n", argv[0]);
  fprintf(stderr, "\n");
  fprintf(stderr, "options:\n");
  fprintf(stderr, "  -h, --help            show this help message and exit\n");
  fprintf(stderr, "  -s SEED, --seed SEED  RNG seed (default: -1)\n");
  fprintf(stderr, "  -t N, --threads N     number of threads to use during computation (default: %d)\n",
          params.n_threads);
  fprintf(stderr, "  -p PROMPT, --prompt PROMPT\n");
  fprintf(stderr, "                        prompt to start generation with (default: random)\n");
  fprintf(stderr, "  -f FNAME, --file FNAME\n");
  fprintf(stderr, "                        load prompt from a file\n");
  fprintf(stderr, "  -tt TOKEN_TEST, --token_test TOKEN_TEST\n");
  fprintf(stderr, "                        test tokenization\n");
  fprintf(stderr, "  -n N, --n_predict N   number of tokens to predict (default: %d)\n", params.n_predict);
  fprintf(stderr, "  --top_k N             top-k sampling (default: %d)\n", params.top_k);
  fprintf(stderr, "  --top_p N             top-p sampling (default: %.1f)\n", params.top_p);
  fprintf(stderr, "  --temp N              temperature (default: %.1f)\n", params.temp);
  fprintf(stderr, "  --repeat-last-n N     last n tokens to consider for penalize (default: %d, 0 = disabled)\n",
          params.repeat_last_n);
  fprintf(stderr, "  --repeat-penalty N    penalize repeat sequence of tokens (default: %.2f, 1.0 = disabled)\n",
          (double) params.repeat_penalty);
  fprintf(stderr, "  -b N, --batch_size N  batch size for prompt processing (default: %d)\n", params.n_batch);
  fprintf(stderr, "  -c N, --context N     context / KV cache size (default: %d)\n", params.n_ctx);
  fprintf(stderr, "  --ignore-eos          ignore EOS token during generation\n");
  fprintf(stderr, "  -ngl N, --gpu-layers N  number of layers to offload to GPU on supported models (default: %d)\n",
          params.n_gpu_layers);
  fprintf(stderr, "  -m FNAME, --model FNAME\n");
  fprintf(stderr, "                        model path (default: %s)\n", params.model.c_str());
  fprintf(stderr, "\n");
}

std::string gpt_random_prompt(std::mt19937 &rng) {
  const int r = rng() % 10;
  switch (r) {
    case 0:
      return "So";
    case 1:
      return "Once upon a time";
    case 2:
      return "When";
    case 3:
      return "The";
    case 4:
      return "After";
    case 5:
      return "If";
    case 6:
      return "import";
    case 7:
      return "He";
    case 8:
      return "She";
    case 9:
      return "They";
    default:
      return "To";
  }

  return "The";
}

std::string trim(const std::string &s) {
  std::regex e("^\\s+|\\s+$");
  return std::regex_replace(s, e, "");
}

std::string replace(const std::string &s, const std::string &from, const std::string &to) {
  std::string result = s;
  size_t pos = 0;
  while ((pos = result.find(from, pos)) != std::string::npos) {
    result.replace(pos, from.length(), to);
    pos += to.length();
  }
  return result;
}

void gpt_vocab::add_special_token(const std::string &token) {
  special_tokens.push_back(token);
}

std::map<std::string, int32_t> json_parse(const std::string &fname) {
  std::map<std::string, int32_t> result;

  // read file into string
  std::string json;
  {
    std::ifstream ifs(fname);
    if (!ifs) {
      fprintf(stderr, "Failed to open %s\n", fname.c_str());
      exit(1);
    }

    json = std::string((std::istreambuf_iterator<char>(ifs)),
                       (std::istreambuf_iterator<char>()));
  }

  if (json[0] != '{') {
    return result;
  }

  // parse json
  {
    bool has_key = false;
    bool in_token = false;

    std::string str_key = "";
    std::string str_val = "";

    int n = json.size();
    for (int i = 1; i < n; ++i) {
      if (!in_token) {
        if (json[i] == ' ') continue;
        if (json[i] == '"') {
          in_token = true;
          continue;
        }
      } else {
        if (json[i] == '\\' && i + 1 < n) {
          if (has_key == false) {
            str_key += json[i];
          } else {
            str_val += json[i];
          }
          ++i;
        } else if (json[i] == '"') {
          if (has_key == false) {
            has_key = true;
            ++i;
            while (json[i] == ' ') ++i;
            ++i; // :
            while (json[i] == ' ') ++i;
            if (json[i] != '\"') {
              while (json[i] != ',' && json[i] != '}') {
                str_val += json[i++];
              }
              has_key = false;
            } else {
              in_token = true;
              continue;
            }
          } else {
            has_key = false;
          }

          str_key = ::replace(str_key, "\\u0120", " "); // \u0120 -> space
          str_key = ::replace(str_key, "\\u010a", "\n"); // \u010a -> new line
          str_key = ::replace(str_key, "\\\"", "\""); // \\\"   -> "

          try {
            result[str_key] = std::stoi(str_val);
          } catch (...) {
            //fprintf(stderr, "%s: ignoring key '%s' with value '%s'\n", fname.c_str(), str_key.c_str(), str_val.c_str());

          }
          str_key = "";
          str_val = "";
          in_token = false;
          continue;
        }
        if (has_key == false) {
          str_key += json[i];
        } else {
          str_val += json[i];
        }
      }
    }
  }

  return result;
}

std::string convert_to_utf8(const std::wstring &input) {
  std::wstring_convert<std::codecvt_utf8<wchar_t>> converter;
  return converter.to_bytes(input);
}


std::wstring convert_to_wstring(const std::string &input) {
  std::wstring_convert<std::codecvt_utf8<wchar_t>> converter;
  return converter.from_bytes(input);
}

void gpt_split_words(std::string str, std::vector<std::string> &words) {
  const std::string pattern = R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)";
  const std::regex re(pattern);
  std::smatch m;

  while (std::regex_search(str, m, re)) {
    for (auto x: m) {
      words.push_back(x);
    }
    str = m.suffix();
  }
}

std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab &vocab, const std::string &text) {
  std::vector<std::string> words;

  // first split the text into words
  {
    std::string str = text;

    // Generate the subpattern from the special_tokens vector if it's not empty
    if (!vocab.special_tokens.empty()) {
      const std::regex escape(R"([\[\\\^\$\.\|\?\*\+\(\)\{\}])");
      std::string special_tokens_subpattern;
      for (const auto &token: vocab.special_tokens) {
        if (!special_tokens_subpattern.empty()) {
          special_tokens_subpattern += "|";
        }
        special_tokens_subpattern += std::regex_replace(token, escape, R"(\$&)");
      }

      std::regex re(special_tokens_subpattern);
      std::smatch m;
      // Split the text by special tokens.
      while (std::regex_search(str, m, re)) {
        // Split the substrings in-between special tokens into words.
        gpt_split_words(m.prefix(), words);
        // Add matched special tokens as words.
        for (auto x: m) {
          words.push_back(x);
        }
        str = m.suffix();
      }
      // Remaining text without special tokens will be handled below.
    }

    gpt_split_words(str, words);
  }

  // find the longest token that forms each word in words:
  std::vector<gpt_vocab::id> tokens;
  for (const auto &word: words) {
    for (int i = 0; i < (int) word.size();) {
      for (int j = word.size() - 1; j >= i; j--) {
        auto cand = word.substr(i, j - i + 1);
        auto it = vocab.token_to_id.find(cand);
        if (it != vocab.token_to_id.end()) { // word.substr(i, j-i+1) in vocab
          tokens.push_back(it->second);
          i = j + 1;
          break;
        } else if (j == i) { // word.substr(i, 1) has no matching
          fprintf(stderr, "%s: unknown token '%s'\n", __func__, word.substr(i, 1).data());
          i++;
        }
      }
    }
  }

  return tokens;
}

std::vector<gpt_vocab::id> parse_tokens_from_string(const std::string &input, char delimiter) {
  std::vector<gpt_vocab::id> output;
  std::stringstream ss(input);
  std::string token;

  while (std::getline(ss, token, delimiter)) {
    output.push_back(std::stoi(token));
  }

  return output;
}

std::map<std::string, std::vector<gpt_vocab::id>> extract_tests_from_file(const std::string &fpath_test) {
  if (fpath_test.empty()) {
    fprintf(stderr, "%s : No test file found.\n", __func__);
    return std::map<std::string, std::vector<gpt_vocab::id>>();
  }

  std::map<std::string, std::vector<gpt_vocab::id>> tests;

  auto fin = std::ifstream(fpath_test, std::ios_base::in);
  const char *delimeter = " => ";
  const char del_tok = ',';
  std::string line;
  while (std::getline(fin, line)) {
    size_t delimiterPos = line.find(delimeter);
    if (delimiterPos != std::string::npos) {
      std::string text = line.substr(0, delimiterPos);
      std::string s_tokens = line.substr(delimiterPos + std::strlen(delimeter));
      tests[text] = parse_tokens_from_string(s_tokens, del_tok);
    }
  }
  return tests;
}

void test_gpt_tokenizer(gpt_vocab &vocab, const std::string &fpath_test) {
  std::map<std::string, std::vector<gpt_vocab::id>> tests = extract_tests_from_file(fpath_test);

  size_t n_fails = 0;

  for (const auto &test: tests) {
    std::vector<gpt_vocab::id> tokens = gpt_tokenize(vocab, test.first);

    if (tokens != test.second) {
      n_fails++;

      // print out failure cases
      fprintf(stderr, "%s : failed test: '%s'\n", __func__, test.first.c_str());
      fprintf(stderr, "%s : tokens in hf:   ", __func__);
      for (const auto &t: test.second) {
        fprintf(stderr, "%s(%d), ", vocab.id_to_token[t].c_str(), t);
      }
      fprintf(stderr, "\n");
      fprintf(stderr, "%s : tokens in ggml: ", __func__);
      for (const auto &t: tokens) {
        fprintf(stderr, "%s(%d), ", vocab.id_to_token[t].c_str(), t);
      }
      fprintf(stderr, "\n");
    }
  }

  fprintf(stderr, "%s : %zu tests failed out of %zu tests.\n", __func__, n_fails, tests.size());
}

bool gpt_vocab_init(const std::string &fname, gpt_vocab &vocab) {
  printf("%s: loading vocab from '%s'\n", __func__, fname.c_str());

  vocab.token_to_id = ::json_parse(fname);

  for (const auto &kv: vocab.token_to_id) {
    vocab.id_to_token[kv.second] = kv.first;
  }

  printf("%s: vocab size = %d\n", __func__, (int) vocab.token_to_id.size());

  // print the vocabulary
  //for (auto kv : vocab.token_to_id) {
  //    printf("'%s' -> %d\n", kv.first.data(), kv.second);
  //}

  return true;
}

gpt_vocab::id gpt_sample_top_k_top_p(
  const gpt_vocab &vocab,
  const float *logits,
  int top_k,
  double top_p,
  double temp,
  std::mt19937 &rng) {
  int n_logits = vocab.id_to_token.size();

  std::vector<std::pair<double, gpt_vocab::id>> logits_id;
  logits_id.reserve(n_logits);

  {
    const double scale = 1.0 / temp;
    for (int i = 0; i < n_logits; ++i) {
      logits_id.push_back(std::make_pair(logits[i] * scale, i));
    }
  }

  // find the top K tokens
  std::partial_sort(
    logits_id.begin(),
    logits_id.begin() + top_k, logits_id.end(),
    [](const std::pair<double, gpt_vocab::id> &a, const std::pair<double, gpt_vocab::id> &b) {
      return a.first > b.first;
    });

  logits_id.resize(top_k);

  double maxl = -INFINITY;
  for (const auto &kv: logits_id) {
    maxl = std::max(maxl, kv.first);
  }

  // compute probs for the top K tokens
  std::vector<double> probs;
  probs.reserve(logits_id.size());

  double sum = 0.0;
  for (const auto &kv: logits_id) {
    double p = exp(kv.first - maxl);
    probs.push_back(p);
    sum += p;
  }

  // normalize the probs
  for (auto &p: probs) {
    p /= sum;
  }

  if (top_p < 1.0f) {
    double cumsum = 0.0f;
    for (int i = 0; i < top_k; i++) {
      cumsum += probs[i];
      if (cumsum >= top_p) {
        top_k = i + 1;
        probs.resize(top_k);
        logits_id.resize(top_k);
        break;
      }
    }

    cumsum = 1.0 / cumsum;
    for (int i = 0; i < (int) probs.size(); i++) {
      probs[i] *= cumsum;
    }
  }

  //printf("\n");
  //for (int i = 0; i < (int) probs.size(); i++) {
  //    printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), probs[i]);
  //}
  //exit(0);

  std::discrete_distribution<> dist(probs.begin(), probs.end());
  int idx = dist(rng);

  return logits_id[idx].second;
}

gpt_vocab::id gpt_sample_top_k_top_p_repeat(
  const gpt_vocab &vocab,
  const float *logits,
  const int32_t *last_n_tokens_data,
  size_t last_n_tokens_data_size,
  int top_k,
  double top_p,
  double temp,
  int repeat_last_n,
  float repeat_penalty,
  std::mt19937 &rng) {

  int n_logits = vocab.id_to_token.size();

  const auto *plogits = logits;

  const auto last_n_tokens = std::vector<int32_t>(last_n_tokens_data, last_n_tokens_data + last_n_tokens_data_size);

  if (temp <= 0) {
    // select the token with the highest logit directly
    float max_logit = plogits[0];
    gpt_vocab::id max_id = 0;

    for (int i = 1; i < n_logits; ++i) {
      if (plogits[i] > max_logit) {
        max_logit = plogits[i];
        max_id = i;
      }
    }
    return max_id;
  }


  std::vector<std::pair<double, gpt_vocab::id>> logits_id;
  logits_id.reserve(n_logits);

  {
    const float scale = 1.0f / temp;
    for (int i = 0; i < n_logits; ++i) {
      // repetition penalty from ctrl paper (https://arxiv.org/abs/1909.05858)
      // credit https://github.com/facebookresearch/llama/compare/main...shawwn:llama:main
      if (repeat_last_n > 0 &&
          std::find(last_n_tokens.end() - repeat_last_n, last_n_tokens.end(), i) != last_n_tokens.end()) {
        // if score < 0 then repetition penalty has to multiplied to reduce the previous token probability
        if (plogits[i] < 0.0f) {
          logits_id.push_back(std::make_pair(plogits[i] * scale * repeat_penalty, i));
        } else {
          logits_id.push_back(std::make_pair(plogits[i] * scale / repeat_penalty, i));
        }
      } else {
        logits_id.push_back(std::make_pair(plogits[i] * scale, i));
      }
    }
  }

  // find the top K tokens
  std::partial_sort(
    logits_id.begin(),
    logits_id.begin() + top_k, logits_id.end(),
    [](const std::pair<double, gpt_vocab::id> &a, const std::pair<double, gpt_vocab::id> &b) {
      return a.first > b.first;
    });

  logits_id.resize(top_k);

  double maxl = -INFINITY;
  for (const auto &kv: logits_id) {
    maxl = std::max(maxl, kv.first);
  }

  // compute probs for the top K tokens
  std::vector<double> probs;
  probs.reserve(logits_id.size());

  double sum = 0.0;
  for (const auto &kv: logits_id) {
    double p = exp(kv.first - maxl);
    probs.push_back(p);
    sum += p;
  }

  // normalize the probs
  for (auto &p: probs) {
    p /= sum;
  }

  if (top_p < 1.0f) {
    double cumsum = 0.0f;
    for (int i = 0; i < top_k; i++) {
      cumsum += probs[i];
      if (cumsum >= top_p) {
        top_k = i + 1;
        probs.resize(top_k);
        logits_id.resize(top_k);
        break;
      }
    }

    cumsum = 1.0 / cumsum;
    for (int i = 0; i < (int) probs.size(); i++) {
      probs[i] *= cumsum;
    }
  }

//    printf("\n");
//    for (int i = 0; i < (int) probs.size(); i++) {
//    for (int i = 0; i < 10; i++) {
//        printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), probs[i]);
//    }

  std::discrete_distribution<> dist(probs.begin(), probs.end());
  int idx = dist(rng);

  return logits_id[idx].second;

}

bool resample(const float *input, size_t inputSampleRate, size_t inputSize,
              std::vector<float> &output, size_t outputSampleRate) {
  // Initialize Converter
  int error;
  SRC_STATE *src_state = src_new(SRC_SINC_FASTEST, 1, &error);
  if (src_state == NULL) {
    fprintf(stderr,"error %s\n",src_strerror(error));
    return false;
  }

  // set convert param
  SRC_DATA src_data;
  src_data.data_in = input;
  src_data.input_frames = inputSize;
  src_data.data_out = new float[inputSize]; // assign size
  src_data.output_frames = inputSize;
  src_data.src_ratio = double(outputSampleRate) / inputSampleRate;

  // convert
  error = src_process(src_state, &src_data);
  if (error) {
    fprintf(stderr,"Error converting sample rate: %d",error);
    delete[] src_data.data_out;
    src_delete(src_state);
    return false;
  }

  // Copy the transformed data into the output vector
  output.assign(src_data.data_out, src_data.data_out + src_data.output_frames_gen);

  // clean
  delete[] src_data.data_out;
  src_delete(src_state);

  return true;
}

bool
read_wav(const std::string &fname, std::vector<float> &pcmf32, std::vector<std::vector<float>> &pcmf32s, bool stereo) {
  drwav wav;
  std::vector<uint8_t> wav_data; // used for pipe input from stdin

  if (fname == "-") {
    {
      uint8_t buf[1024];
      while (true) {
        const size_t n = fread(buf, 1, sizeof(buf), stdin);
        if (n == 0) {
          break;
        }
        wav_data.insert(wav_data.end(), buf, buf + n);
      }
    }

    if (drwav_init_memory(&wav, wav_data.data(), wav_data.size(), nullptr) == false) {
      fprintf(stderr, "error: failed to open WAV file from stdin\n");
      return false;
    }

    fprintf(stderr, "%s: read %zu bytes from stdin\n", __func__, wav_data.size());
  } else if (drwav_init_file(&wav, fname.c_str(), nullptr) == false) {
    fprintf(stderr, "error: failed to open '%s' as WAV file\n", fname.c_str());
    return false;
  }

  if (wav.channels != 1 && wav.channels != 2) {
    fprintf(stderr, "%s: WAV file '%s' must be mono or stereo\n", __func__, fname.c_str());
    return false;
  }

  if (stereo && wav.channels != 2) {
    fprintf(stderr, "%s: WAV file '%s' must be stereo for diarization\n", __func__, fname.c_str());
    return false;
  }

  if (wav.sampleRate != COMMON_SAMPLE_RATE) {
    fprintf(stderr, "%s: WAV file '%s' must be %i kHz\n", __func__, fname.c_str(), COMMON_SAMPLE_RATE / 1000);
    return false;
  }

  if (wav.bitsPerSample != 16) {
    fprintf(stderr, "%s: WAV file '%s' must be 16-bit\n", __func__, fname.c_str());
    return false;
  }

  const uint64_t n = wav_data.empty() ? wav.totalPCMFrameCount : wav_data.size() /
                                                                 (wav.channels * wav.bitsPerSample / 8);

  std::vector<int16_t> pcm16;
  pcm16.resize(n * wav.channels);
  drwav_read_pcm_frames_s16(&wav, n, pcm16.data());
  drwav_uninit(&wav);

  // convert to mono, float
  pcmf32.resize(n);
  if (wav.channels == 1) {
    for (uint64_t i = 0; i < n; i++) {
      pcmf32[i] = float(pcm16[i]) / 32768.0f;
    }
  } else {
    for (uint64_t i = 0; i < n; i++) {
      pcmf32[i] = float(pcm16[2 * i] + pcm16[2 * i + 1]) / 65536.0f;
    }
  }

  if (stereo) {
    // convert to stereo, float
    pcmf32s.resize(2);

    pcmf32s[0].resize(n);
    pcmf32s[1].resize(n);
    for (uint64_t i = 0; i < n; i++) {
      pcmf32s[0][i] = float(pcm16[2 * i]) / 32768.0f;
      pcmf32s[1][i] = float(pcm16[2 * i + 1]) / 32768.0f;
    }
  }

  return true;
}

bool read_mp3(const std::string &fname, std::vector<float> &pcmf32, bool stereo) {
  drmp3 mp3;
  if (!drmp3_init_file(&mp3, fname.c_str(), nullptr)) {
    fprintf(stderr, "error: failed to open '%s' as MP3 file\n", fname.c_str());
    return false;
  }

  if (mp3.channels != 1 && mp3.channels != 2) {
    fprintf(stderr, "%s: MP3 file '%s' must be mono or stereo\n", __func__, fname.c_str());
    return false;
  }
  if (stereo && mp3.channels != 2) {
    fprintf(stderr, "%s: MP3 file '%s' must be stereo for this operation\n", __func__, fname.c_str());
    return false;
  }


  drmp3_uint64 frameCount;
  float *pSampleData = drmp3__full_read_and_close_f32(&mp3, nullptr, &frameCount);
  bool isAllocated = false;
  fprintf(stdout, "mp3.channels %d,mp3.sampleRate %d, frameCount:%llu\n", mp3.channels, mp3.sampleRate, frameCount);

  if (!stereo && mp3.channels == 2) {
    std::vector<float> monoData;
    monoData.reserve(frameCount);
    for (drmp3_uint64 i = 0; i < frameCount * 2; i += 2) {
      monoData.push_back((pSampleData[i] + pSampleData[i + 1]) / 2);
    }
    drmp3_free(pSampleData, nullptr); // Releasing raw data

    pSampleData = new float[monoData.size()]; // reallocate memory
    std::copy(monoData.begin(), monoData.end(), pSampleData); // copy data
    isAllocated = true;
    frameCount = monoData.size();
    mp3.channels = 1;  // Update the number of channels
  }

  printf("mp3.channels %d,mp3.sampleRate %d, frameCount:%llu\n", mp3.channels, mp3.sampleRate, frameCount);
  if (mp3.sampleRate != COMMON_SAMPLE_RATE) {
    std::vector<float> resampledData;
    if (!resample(pSampleData, mp3.sampleRate, frameCount, resampledData, COMMON_SAMPLE_RATE)) {
      fprintf(stderr, "error: failed to resample MP3 data\n");
      delete[] pSampleData; // Releasing reallocated memory
      return false;
    }
    pcmf32.swap(resampledData); // Use of transformed data
  } else {
    pcmf32.assign(pSampleData, pSampleData + frameCount);
  }
  //release
  if (isAllocated) {
    delete[] pSampleData; // If memory is reallocated, use the delete[]
  } else {
    drmp3_free(pSampleData, nullptr); // otherwise, use the drmp3_free
  }
  return true;
}

void high_pass_filter(std::vector<float> &data, float cutoff, float sample_rate) {
  const float rc = 1.0f / (2.0f * M_PI * cutoff);
  const float dt = 1.0f / sample_rate;
  const float alpha = dt / (rc + dt);

  float y = data[0];

  for (size_t i = 1; i < data.size(); i++) {
    y = alpha * (y + data[i] - data[i - 1]);
    data[i] = y;
  }
}

bool
vad_simple(std::vector<float> &pcmf32, int sample_rate, int last_ms,
           float vad_thold, float freq_thold, bool verbose) {
  const int n_samples = pcmf32.size();
  const int n_samples_last = (sample_rate * last_ms) / 1000;

  if (n_samples_last >= n_samples) {
    // not enough samples - assume no speech
    return false;
  }

  if (freq_thold > 0.0f) {
    high_pass_filter(pcmf32, freq_thold, sample_rate);
  }

  float energy_all = 0.0f;
  float energy_last = 0.0f;

  for (int i = 0; i < n_samples; i++) {
    energy_all += fabsf(pcmf32[i]);

    if (i >= n_samples - n_samples_last) {
      energy_last += fabsf(pcmf32[i]);
    }
  }

  energy_all /= n_samples;
  energy_last /= n_samples_last;

  if (verbose) {
    fprintf(stderr, "%s: energy_all: %f, energy_last: %f, vad_thold: %f, freq_thold: %f\n", __func__, energy_all,
            energy_last, vad_thold, freq_thold);
  }

  if (energy_last > vad_thold * energy_all) {
    return false;
  }

  return true;
}

float similarity(const std::string &s0, const std::string &s1) {
  const size_t len0 = s0.size() + 1;
  const size_t len1 = s1.size() + 1;

  std::vector<int> col(len1, 0);
  std::vector<int> prevCol(len1, 0);

  for (size_t i = 0; i < len1; i++) {
    prevCol[i] = i;
  }

  for (size_t i = 0; i < len0; i++) {
    col[0] = i;
    for (size_t j = 1; j < len1; j++) {
      col[j] = std::min(std::min(1 + col[j - 1], 1 + prevCol[j]),
                        prevCol[j - 1] + (i > 0 && s0[i - 1] == s1[j - 1] ? 0 : 1));
    }
    col.swap(prevCol);
  }

  const float dist = prevCol[len1 - 1];

  return 1.0f - (dist / std::max(s0.size(), s1.size()));
}

bool sam_params_parse(int argc, char **argv, sam_params &params) {
  for (int i = 1; i < argc; i++) {
    std::string arg = argv[i];

    if (arg == "-s" || arg == "--seed") {
      params.seed = std::stoi(argv[++i]);
    } else if (arg == "-t" || arg == "--threads") {
      params.n_threads = std::stoi(argv[++i]);
    } else if (arg == "-m" || arg == "--model") {
      params.model = argv[++i];
    } else if (arg == "-i" || arg == "--inp") {
      params.fname_inp = argv[++i];
    } else if (arg == "-o" || arg == "--out") {
      params.fname_out = argv[++i];
    } else if (arg == "-h" || arg == "--help") {
      sam_print_usage(argc, argv, params);
      exit(0);
    } else {
      fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
      sam_print_usage(argc, argv, params);
      exit(0);
    }
  }

  return true;
}

void sam_print_usage(int /*argc*/, char **argv, const sam_params &params) {
  fprintf(stderr, "usage: %s [options]\n", argv[0]);
  fprintf(stderr, "\n");
  fprintf(stderr, "options:\n");
  fprintf(stderr, "  -h, --help            show this help message and exit\n");
  fprintf(stderr, "  -s SEED, --seed SEED  RNG seed (default: -1)\n");
  fprintf(stderr, "  -t N, --threads N     number of threads to use during computation (default: %d)\n",
          params.n_threads);
  fprintf(stderr, "  -m FNAME, --model FNAME\n");
  fprintf(stderr, "                        model path (default: %s)\n", params.model.c_str());
  fprintf(stderr, "  -i FNAME, --inp FNAME\n");
  fprintf(stderr, "                        input file (default: %s)\n", params.fname_inp.c_str());
  fprintf(stderr, "  -o FNAME, --out FNAME\n");
  fprintf(stderr, "                        output file (default: %s)\n", params.fname_out.c_str());
  fprintf(stderr, "\n");
}
